• 한국생산제조학회지
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• 제9권1호
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• pp.119-127
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• 2000

In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

• 비파괴검사학회지
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• 제20권2호
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• pp.102-109
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• 2000

본 논문에서는 체적형 결함과 균열형 결함에 대한초음파산란 현상을 모델링하기 위한 두가지 이론을 설명하였다. 동탄성 Kirchhoff 근사 (EKA)와 기하학적 회절이론 (GTD)이 각각 원주형 기공과 반무한 균열에 적용되었다. 이 두 이론은 고주파수 근사법으로 알려져 있다. 모델 결함들에 평면파가 입사하는 경우의 2차원 동탄성 산란 문제를 고려하였으며 산란장을 반사계수와 회절계수의 항으로 구하였다. 원거리에서 산란파의 변위에 대한 입사파 변위의 비를 관찰 방향의 함수로 구했으며 그 결과를 경계요소법과 비교하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.